Fast, dynamic, data-driven report deployment of data mining and predictive insight into business intelligence (bi) tools

ABSTRACT

A Business Intelligence (BI) meta model template is selected based on one or more meta model object types in a model structure. A BI meta model is generated from the selected BI meta model template. One or more BI report specification templates are selected based on a mining model type. A BI report specification is generated from the selected one or more BI report specification templates, a schema of the model structure, and content of the model structure. The BI meta model and the BI report specification are deployed to a BI server for use in generating a BI report using a BI tool at the BI server. In response to a user request for a BI report, the BI report is generated with a BI tool at the BI server that uses the BI meta model and the BI report specification.

BACKGROUND

1. Field

Embodiments of the invention relate to fast, dynamic, data-driven report deployment of data mining and predictive insight into Business Intelligence (BI) tools.

2. Description of the Related Art

Data mining results and insights are different from data that is typically stored in flat table structures. Therefore, the data mining results and insights are mostly stored as data mining models (also referred to as “mining models”) in hierarchical ways in large documents (e.g. standardized Predictive Model Markup Language (PMML) format). However, many conventional Business Intelligence (BI) tools can not consume those data mining models. BI tools may be described as analyzing data and presenting reports (e.g. report design tools). Therefore, the mining results and insights need to be transformed to a form that is consumable by the BI tools.

Few vendors provide dedicated BI tools (e.g. report design tools) in which a report designer can manually create mining results and insights reports (i.e. mining reports). Because vendors do not provide dedicated BI tools, the user has to transform mining results and insights into a form consumable by the BI Tools. Further, deep data mining knowledge is required to create reports with the general BI tools. Nevertheless, the creation of such reports is a tedious task and changes in the underlying data result in long lasting manual changes. Further, the task of transforming the mining results and insights and creating the reports and meta information requires deep knowledge in the involved tools and software, as well as, deep mining skills to know how to visualize those mining insights.

Known solutions are based on exporting images that were generated within the mining tool. Then, the images are incorporated into the report in a static manner (e.g. similar to using an image within a web page). However, this is a very static and non-interactive way. Further, this solution does not provide automatic deployment of the mining results and insights.

Most tools do not allow visualizing standardized data mining models natively. Thus, such tools are less flexible and restrict the visualization to predefined graphics.

BRIEF SUMMARY

Provided are techniques for processing Business Intelligence (BI) reports. A set of BI meta model templates and BI report specification templates are provided. A Business Intelligence (BI) meta model template is selected from the set of BI meta model templates based on one or more meta model object types in a model structure. A BI meta model is generated from the selected BI meta model template. One or more BI report specification templates are selected from the set of BI report specification templates based on a mining model type. A BI report specification is generated from the selected one or more BI report specification templates, a schema of the model structure, and content of the model structure. The BI meta model and the BI report specification are deployed to a BI server for use in generating a BI report using a BI tool at the BI server. In response to a user request for a BI report, the BI report is generated with a BI tool at the BI server that uses the BI meta model and the BI report specification.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Referring now to the drawings in which like reference numbers represent corresponding parts throughout:

FIG. 1 illustrates a computing environment in accordance with certain embodiments.

FIG. 2 illustrates a partial example of an input data table in accordance with certain embodiments.

FIG. 3 illustrates a partial example of a mining model in PMML format in accordance with certain embodiments. FIG. 3 is formed by FIGS. 3A, 3B, and 3C.

FIG. 4 illustrates a partial example of a model table in accordance with certain embodiments.

FIG. 5 illustrates an example of a BI meta model in accordance with certain embodiments. FIG. 5 is formed by FIGS. 5A, 5B, 5C, and 5D.

FIG. 6 illustrates an example of a report specification in accordance with certain embodiments. FIG. 6 is formed by FIGS. 6A, 6B, 6C, 6D, 6E, 6F, 6G, 6H, 6I, 6J, and 6K.

FIG. 7 illustrates a partial example of a stored procedure in accordance with certain embodiments. FIG. 7 is formed by FIGS. 7A and 7B.

FIG. 8 illustrates automatic deployment in accordance with certain embodiments.

FIG. 9 illustrates an example BI meta model template in accordance with certain embodiments. FIG. 9 is formed by FIGS. 9A, 9B, 9C, 9D, 9E, 9F, 9G, 9H, and 9I.

FIG. 10 illustrates an example BI report specification template in accordance with certain embodiments. FIG. 10 is formed by FIGS. 10A, 10B, 10C, 10D, 10E, 10F, 10G, 10H, and 10I.

FIG. 11 illustrates an automatic deployment procedure with dynamic invocation from a user in accordance with certain embodiments.

FIG. 12 illustrates data mining models in a screen in accordance with certain embodiments.

FIG. 13 illustrates specification of a BI tool in a screen in accordance with certain embodiments.

FIG. 14 illustrates logon credentials in a screen in accordance with certain embodiments.

FIG. 15 illustrates selection of a destination within the BI tool in screen in accordance with certain embodiments.

FIG. 16 illustrates schema table information in a screen in accordance with certain embodiments.

FIGS. 17 and 18 illustrate progression in screens in accordance with certain embodiments.

FIG. 19 illustrates a list of reports in screen with the mining insight that are available in the BI tool in accordance with certain embodiments.

FIGS. 20, 21, and 23 illustrate various reports in screens with the mining insight that are available in the BI tool in accordance with certain embodiments.

FIG. 23 illustrates, in a flow diagram, logic performed by the deployment system 120 in accordance with certain embodiments.

FIG. 24 illustrates, in a flow diagram, logic performed by the BI client and the BI server in accordance with certain embodiments.

FIG. 25 illustrates, in a flow diagram, logic performed by the deployment system using a stored procedure in accordance with certain embodiments.

FIG. 26 illustrates, in a flow diagram, logic performed by the BI client and the BI server using a stored procedure in accordance with certain embodiments.

FIG. 27 illustrates a computer architecture that may be used in accordance with certain embodiments.

DETAILED DESCRIPTION

In the following description, reference is made to the accompanying drawings which form a part hereof and which illustrate several embodiments of the invention. It is understood that other embodiments may be utilized and structural and operational changes may be made without departing from the scope of the invention.

FIG. 1 illustrates a computing environment in accordance with certain embodiments. A computing device 110 includes a deployment system 120, at least one BI package 130, one or more BI meta model templates 142 and one or more report specification templates 144. Each BI package 130 includes a BI meta model 132 (i.e. meta information) and one or more BI report specifications 134. In certain embodiments, each BI package 130 may include multiple BI meta models.

The computing device is coupled to a data store 150. The data store 150 includes one or more data structures 152, one or more mining models 154 (also referred to as “data mining models”), one or more model structures 156, and executable code 160 (e.g. one or more stored procedures). Although stored procedures may be used in examples herein, any form of executable code 160 may be used instead or in addition to the stored procedures. In certain embodiments, the data store 150 is a database. In certain embodiments, the data structures 152 are data tables. In certain embodiments, the model structures 156 are model tables. In certain embodiments, the mining models are in PMML format.

The computing device 110 is also coupled to a BI server 170, which is coupled to a BI client 180. The BI server 170 includes one or more BI tools 172 and a repository 174. The repository 174 stores a copy of each BI package 130 and stores one or more BI reports 176.

The deployment system 120 automatically generates reports based on the one or more data structures 152, one or more mining models 154, and one or more model structures 156 stored in the data store 150 and deploys them automatically to a BI tool 172 at the BI server 170. The deployment system 120 enables a single user without deep knowledge of data mining to generate the reports and accelerate the process of creating the reports.

The deployment system 120 enables automatic creation of BI reports 176 presenting data mining and/or predictive insights. Initially, the deployment system 120 automatically creates a table representation (e.g. one or more model structures 156) from the one or more mining models 154 and extracts the table representation to a database (e.g. the data store 150). In certain embodiments, the content of the mining model 154 is extracted into at least one model structure 156 having a schema dependent on the mining model. Next, the deployment system 120 generates a BI package 130 including a BI meta model 132 (i.e. meta information) and one or more BI report specifications 134 required by most BI tools 172. Finally, the deployment system 120 automatically deploys the BI package 130 to the BI tools 172.

FIG. 2 illustrates a partial example of an input data table 200 in accordance with certain embodiments. Table 200 is a partial example of a data structure 152. Table 200 contains already prepared input data for a clustering scenario (customer segmentation). The records represent bank customers with demographic, product related data, and transaction related data. In table 200, there are eight records, each with eleven columns of data.

FIG. 3 illustrates a partial example of a mining model 300, 310, 320 in PMML format in accordance with certain embodiments. FIG. 3 is formed by FIGS. 3A, 3B, and 3C. Data mining model 300 is an example of a mining model 154. The mining model 300 is in eXtensible Markup Language (XML) and represents a PMML data mining clustering model for the data table 200.

FIG. 4 illustrates a partial example of a model table 400 in accordance with certain embodiments. Model table 400 is an example of a model structure 156. The model table 400 contains parts of the previous mining model 300, 310, 320. In this example, the distribution statistics of the single clusters are represented. The model table 400 is accessed by the BI reports.

FIG. 5 illustrates an example of a BI meta model 500, 510, 520, 530 in accordance with certain embodiments. FIG. 5 is formed by FIGS. 5A, 5B, 5C, and 5D.

The BI meta model 500, 510, 520, 530 is an example of a BI meta model 132. The BI meta model 500, 510, 520, 530 is in XML and represents a model specification that includes a description of model table 400.

FIG. 6 illustrates an example of a BI report specification 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, and 695 in accordance with certain embodiments. FIG. 6 is formed by FIGS. 6A, 6B, 6C, 6D, 6E, 6F, 6G, 6H, 6I, 6J, and 6K. The BI report specification 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, and 695 represents a BI report specification 134. The BI report specification 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, and 695 is in XML and renders the data of the model table in a semantically useful way.

FIG. 7 illustrates a partial example of a stored procedure 700, 710 in accordance with certain embodiments. The stored procedure 700, 710 is an example of executable code 160. The stored procedure 700, 710 encapsulates processing to perform a clustering. In the stored procedure 700, 710, there is one parameter allowing passing of a maximum number of clusters value.

FIG. 8 illustrates automatic deployment in accordance with certain embodiments. The deployment system 120 automatically creates BI reports presenting data mining and/or predictive insight. The data mining and/or predictive insight is created by a data mining user implementing a mining flow that includes data preparation and the actual modeling (i.e. which executes the mining technique to create the mining model 812 from the data tables 810 and store the mining model 812 in the database 800).

The deployment system 120 automatically creates a table representation from the mining model 812 and extracts that table representation to the database 800 as a model table 814. This table representation is done in a fashion such that the data mining insight can be accessed and understood by a BI tool.

The deployment system 120 generates a BI package 850 that includes (1) meta information (a BI meta model) and (2) a BI report specification required by the BI tool. The meta information and the report specification are dynamically created based on the content of the mining model 812 and the schema and data of the model table 814 containing the mining insight.

These reports are often static, based on the information contained in the mining model 812. As the insight is contained in the model table 814, the insight can be updated by re-executing the processing marked with an “R” in FIG. 8. This creates new insight (e.g. if the underlying data in the data tables 810 changes). This processing can also be incorporated easily into automatic business processes.

Finally, the deployment system 120 automatically deploys the BI package 850 to the BI server 870. The deployment system 120 uses the BI tool's Application Programming Interfaces (API) to deploy the generated BI meta model and BI report specification without manual user interaction. The deployment system 120 also triggers creation of the actual report from the report specification within the BI server 870. Then, the user can access the mining and/or predictive insight like any other report using the BI client 880. The BI server 870 retrieves the mining insight directly from the model table 814. Further automation may include automatic distribution of the reports using other channels (e.g. email).

With reference to FIG. 1, in certain embodiments, the BI meta model 132 generation is based on the BI meta model templates 142. On the other hand, a conventional meta model designer may start from scratch. The BI meta model templates 142 contain basic structures for the BI meta model 132. First, the deployment system 120 analyzes the structure of the model structure 156. From this analysis, the deployment system 120 derives BI meta model objects (e.g. query subjects (abstract views of tables), relationships (determining how several query subjects relate to each other), determinants (defining different levels of granularity on a query subject), etc.). Second, the deployment system 120 analyses the actual data to derive meta model object types (e.g. measures vs. dimensions or a time hierarchy is created in case columns of type date are involved). In certain embodiments, the BI meta model template 142 is selected based on the meta model object types. In certain embodiments, the BI meta model template 142 may be selected based on the mining technique and use case scenario. FIG. 9 illustrates an example BI meta model template 900, 910, 920, 930, 940, 950, 960, 970, and 980 in accordance with certain embodiments. FIG. 9 is formed by FIGS. 9A, 9B, 9C, 9D, 9E, 9F, 9G, 9H, and 9I. The BI meta model template 900, 910, 920, 930, 940, 950, 960, 970, and 980 is an example of a BI meta model template 142.

A model includes an abstract representation of the data source structures (e.g. tables in a relational database), relationships between those representations, information on how to aggregate the data, a preferred language to be used, calculations, filters, folders, etc. Thus, a model may be described as an abstraction layer over the data source, which can be enhanced with more information. The elements of the model can then be used (i.e. referenced) in the report specification.

In certain embodiments, the BI report specification 134 generation is based on the BI report specification templates 144. On the other hand, a conventional report designer may start from scratch. The BI report specification templates 144 contain basic structures for the BI report specification 134 depending on the mining model type (i.e. data mining functions, such as, clustering, classification, association, regression, sequence rules, time series, etc.). The different data mining functions create different data mining models.

The association data mining functions may be described as finding items in data that are associated with each other in a meaningful way. With the classification data mining functions, a user can create, validate, or test classification models (e.g. analyze why a certain classification was made or predict a classification for new data). The clustering data mining function may be described as searching the input data for characteristics that frequently occur in common and groups the input data into clusters, where the members of each cluster have similar properties.

Regression is similar to classification except for the type of the predicted value. For example, classification predicts a class label, while regression predicts a numeric value. Moreover, regression also can determine the input fields that are most relevant to predict the target field values. The predicted value might not be identical to any value contained in the data that is used to build the model. An example application is customer ranking by expected profit.

The sequence rules data mining function may be described as finding typical sequences of events in data. The time series data mining function may be described as enabling forecasting of time series values.

In certain embodiments, based on the mining model type, one or more report specification templates are available. The user may choose between the available ones. During BI report specification 134 generation, the deployment system 120 analyzes the content of the model structure 156. For example, the data of the model structure 156 is analyzed for the number of features that define a clustering. The deployment system 120 detects for each cluster the most relevant features that describe each cluster. Only those most relevant features are incorporated into the BI report specification 134. Then, the deployment system 120 replicates the BI report specification template 144 with the most relevant features in their relevant order.

FIG. 10 illustrates an example BI report specification template 1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, and 1080 in accordance with certain embodiments. FIG. 10 is formed by FIGS. 10A, 10B, 10C, 10D, 10E, 10F, 10G, 10H, and 10I. The BI report specification template 1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, and 1080 is an example of a BI report specification template 144.

In certain embodiments, the deployment system 120 analyzes the model structure 156 for the relevant information and incorporates this content in an optimal way using the BI report specification templates 144.

Furthermore, the formatting of the reports and charts is optimized based on the data of the model structure 156. For example, different charts may result in different axis scaling as the data within those charts may vary. The charts in BI tools 172 will then be optimized for the underlying data. Due to the analysis of the deployment system 120, the optimal axis scaling can be determined in advance, which allows for better comparison and understanding of the mining model.

For each mining model type, there may exist several BI report specifications that are linked with each other. For example, detail reports for dedicated charts or drill through reports may be linked.

In case the mining model 154 is re-created, the deployment system 120 performs analysis of the mining model table that may result in a different formatting and layout of the reports and charts. Then, the previous BI report specification 134 may not have the optimal layout for the new mining model 154. Therefore, the deployment system 120 automatically re-generates the BI report specification 134 to ensure optimal layouts of the reports and charts.

Often users need to know details from the underlying data from which the mining model 154 was generated. Thus, the deployment system 120 automatically incorporates drill through data into the reports, allowing for better understanding of the mining model.

Most useful are those data items that represent typical examples. For example, clustering methods that create homogenous groups with similar characteristics. Typical examples are those data items which best represent the characteristic of a certain cluster. The deployment system 120 automatically detects those typical data items and incorporates them into the report.

The deployment system 120 reduces manual human effort from hours or days to seconds. Further, deployment system 120 allows performing this task by a single person without any expert knowledge of any of the multiple tools involved or the mining model 154. Especially in cases where data or the complete structure of the mining model 154 is changing often, and thus, manual changes are required, large cost and time savings are reached.

FIG. 11 illustrates an automatic deployment procedure with dynamic invocation from a user in accordance with certain embodiments. The deployment system 120 allows creation and deployment of dynamic reports which invoke mining of data at the time a user (e.g. a report consumer at the BI client 180) interacts with the BI tool. This allows the user to customize the reports by passing data mining parameters and/or other settings.

In FIG. 11, a stored procedure is stored in the database 1100. In certain embodiments, a mining expert defines processing of the stored procedure and what parameter fields are dynamic, and the deployment system 120 automatically creates the stored procedure 1110 from the mining flow, which was created by the data preparation and modeling. In particular, the deployment system 120 creates the stored procedure in block 1120 (generate stored procedure from mining flow) using data from block 1122 (data preparation), block 1124 (modeling), and block 1126 (extract model content into table).

The stored procedure 1110 can then be invoked by the BI server 1170 passing the data mining parameters entered by the user using the BI client 1180. The dynamically created mining insight is then retrieved by the BI server 1170 from the result set returned by the stored procedure 1110.

In certain embodiments, the stored procedure 1110 generation is based on the data preparation and mining flows defined by a mining expert. The deployment system 120 converts the flow into Structured Query Language (SQL) statements and further incorporates data mining parameters defined by the user. Those data mining parameters are defined as input for the stored procedure 1110 and are incorporated at the proper positions within the SQL body. The user invokes the report, then the BI server 1170 invokes the stored procedure 1170 and passes the data mining parameters. The complex flow is transparent for the user. The stored procedure 1110 returns data in the same format as the model table.

FIGS. 12-22 illustrate user interaction in accordance with certain embodiments. In certain embodiments, selected elements may be highlighted in the screens shown in FIGS. 12-22. In FIGS. 12-22, for ease of reference, selected items may be shown with dotted or bold lines.

FIG. 12 illustrates data mining models in a screen 1200 in accordance with certain embodiments. A user selects the data mining model 1210 to be deployed in the data mining tool.

FIG. 13 illustrates specification of a BI tool in a screen 1300 in accordance with certain embodiments. The user specifies the BI tool 1310 to which the user wants to deploy the data mining model. FIG. 14 illustrates logon credentials in a screen 1400 in accordance with certain embodiments. In the screen 1400, the user provides necessary logon credentials.

FIG. 15 illustrates selection of a destination within the BI tool in screen 1500 in accordance with certain embodiments. The user selects the destination 1510 within the BI tool to which deploy the BI package and reports. Optionally, names of the generated meta information and reports can be adapted.

FIG. 16 illustrates schema table information in a screen 1600 in accordance with certain embodiments. In screen 1600, optionally, the user may adapt the name of the model structures 156 to which the data mining insight is extracted.

FIGS. 17 and 18 illustrate progression in screens 1700, 1800 in accordance with certain embodiments. A user selects the Finish button 1710, and then the deployment system 120 automatically generates a BI package 130 for the selected data mining model. This is a data-driven process.

FIG. 19 illustrates a list of reports in screen 1900 with the mining insight that are available in the BI tool in accordance with certain embodiments. Clicking on the reports 1910, 1920, 1930 allows the user to browse the mining information.

FIGS. 20, 21, and 22 illustrate various reports in screens 2000, 2100, 2110, 2200, 2210 with the mining insight that are available in the BI tool in accordance with certain embodiments.

FIG. 23 illustrates, in a flow diagram, logic performed by the deployment system 120 in accordance with certain embodiments. Control begins at block 2300 with the deployment system 120 creating a mining model 154 by modeling data in one or more data structures 152. In block 2302, the deployment system 120 extracts the content of the mining model 154 into a model structure 156. In block 2304, the deployment system 120 selects a BI meta model template 142 based on one or more meta model object types (e.g. measures vs. dimensions or a time hierarchy is created in case columns of type date are involved) in the model structure 156. In block 2306, the deployment system 120 generates a BI meta model 132 from the selected BI meta model template 142. In block 2308, the deployment system 120 selects a BI report specification template 144 based on a mining model type (e.g. clustering, classification, association, etc.). In block 2310, the deployment system 120 generates the BI report specification 134 from the selected BI report specification 144, the model structure 156 schema, and the model structure 156 content. In block 2312, the deployment system 120 creates a BI package 130 with BI meta model 132 and the BI report specification 134. In block 2314, the deployment system 120 deploys the BI package 130 to the repository 174 of the BI server 170.

FIG. 24 illustrates, in a flow diagram, logic performed by the BI client 180 and the BI server 170 in accordance with certain embodiments. Control begins at block 2400 with a user at the BI client 180 requesting a report. In block 2402, the BI client 180 forwards the request to the BI server 170. In block 2404, the BI server 170 uses a BI tool 174 that uses the mining model 154 (which the BI server 170 retrieves from the computing device 110) and the BI package stored in the repository 174 to generate the BI report 176. In block 2406, the BI server 170 sends the BI report 176 to the BI client 180. In block 2408, the BI client 180 displays the BI report 176 to the user.

FIG. 25 illustrates, in a flow diagram, logic performed by the deployment system 120 using a stored procedure in accordance with certain embodiments. Control begins at block 2500 with the deployment system 120 creating executable code 160 (e.g. a stored procedure). The executable code, when invoked or executed with data mining parameters and/or settings provided by a user, creates the mining model 154 by modeling data in one or more data structures 152 and extracts the content of the mining model 154 into a model structure 156. In block 2502, the deployment system 120 selects a BI meta model template 142 based on one or more meta model object types (e.g. measures vs. dimensions or a time hierarchy is created in case columns of type date are involved) in the model structure 156. In block 2504, the deployment system 120 generates a BI meta model 132 from the selected BI meta model template 142. In block 2506, the deployment system 120 selects a BI report specification template 144 based on a mining model type (e.g. clustering, classification, association, etc.) In block 2508, the deployment system 120 generates the BI report specification 134 from the selected BI report specification 144, the model structure 156 schema, and the model structure 156 content. In block 2510, the deployment system 120 creates a BI package 130 with BI meta model 132 and the BI report specification 134. In block 2512, the deployment system 120 deploys the BI package 130 to the repository 174 of the BI server 170.

FIG. 26 illustrates, in a flow diagram, logic performed by the BI client 180 and the BI server 170 using a stored procedure in accordance with certain embodiments.

Control begins at block 2600 with a user at the BI client 180 requesting a report. In block 2602, the BI client 180 forwards the request to the BI server 170. In block 2604, the BI server 170 invokes (executes) the executable code 160 with one or more parameters provided by a user to retrieve the mining model 154 dynamically and uses the BI package 130 stored in the repository 174 to generate the BI report 176. In block 2606, the BI server 170 sends the BI report 176 to the BI client 180. In block 2608, the BI client 180 displays the BI report 176 to the user.

Thus, the deployment system 120 allows for automatic, fast and data-driven deployment of data mining results to BI tools 172. The deployment system 120 abstracts the user in a fast and intuitive fashion from the complexity of the underlying various processes. Therefore, a single user without deep mining skills can perform the deployment. This accelerates and simplifies the deployment process, and thus, saves time and costs.

The deployment system 120 enables deployment of mining models 154 in PMML format (also referred to as “mining PMML models”) in BI tools 172. The deployment system 120 automates the process such that it is easier to deploy mining models 154 and data mining itself (e.g. data in data structures 152) in BI tools 174.

Certain embodiments process BI reports in a computing system that contains (i) a database system for containing raw data in data structures 152, carrying out data mining, and storing data mining results in mining models 154, and (ii) a BI server 170 containing a repository 174 for storing information defining structure and content of BI reports (e.g. BI meta models and BI report specifications). A set of BI templates (e.g. a set of BI meta model templates 142 and a set of BI report specification templates 144) are provided. The deployment system 120 prepares data for data mining, generates a data mining model, extracts the data mining model content into at least one table having a model table schema dependent on the mining model, and stores the at least one table in the database.

In response to storing the at least one table in the database, the deployment system 120 selects a BI template based on the type of the model, analyses the model table schema and the model table content, generates information defining the structure and content of a report based on the results of the analysis and on the selected BI template, and deploys the information defining the structure and content of a BI report 176 at the BI server 170.

In response to a user request, the BI report 176 is generated from the information defining the structure and content of the BI report 176 and the BI report 176 is delivered to the user from the BI server 170.

In certain embodiments, a piece of executable code 160 (e.g. a stored procedure) is stored in the data store 150, and execution of the piece of executable code triggers, in response to receiving data mining parameters from a user, generation of the data mining model in accordance with the received data mining parameters and extraction of the data mining model content into the at least one table.

In certain embodiments, the data preparation and the data mining model generation are monitored and repeated. In certain embodiments, the piece of executable code is used for repeating the data preparation and data mining model generation based on the monitoring and is generated by the deployment system 120. The input data that is used to compute a mining model, previously going through the data preparation phase can be monitored. In certain embodiments, if new data comes in, or if the current data changes, the data preparation, the modeling, and the extraction of resulting data mining model can be automatically started. In certain alternative embodiments, this processing can be started periodically, instead of triggered by a change in the input data.

Additional Embodiment Details

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, solid state memory, magnetic tape or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Aspects of the embodiments of the invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational processing (e.g. operations or steps) to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The code implementing the described operations may further be implemented in hardware logic or circuitry (e.g. an integrated circuit chip, Programmable Gate Array (PGA), Application Specific Integrated Circuit (ASIC), etc. The hardware logic may be coupled to a processor to perform operations. For example, the deployment system 120 may be implemented in hardware logic or a combination of software and hardware logic.

The deployment system 120 may be implemented as hardware (e.g. hardware logic or circuitry), software, or a combination of hardware and software.

FIG. 27 illustrates a computer architecture 2700 that may be used in accordance with certain embodiments. Computing device 110, BI server 170, 870, 970, and/or BI client 180, 280, 980 may implement computer architecture 2700. The computer architecture 2700 is suitable for storing and/or executing program code and includes at least one processor 2702 coupled directly or indirectly to memory elements 2704 through a system bus 2720. The memory elements 2704 may include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. The memory elements 2704 include an operating system 2705 and one or more computer programs 2706.

Input/Output (I/O) devices 2712, 2714 (including but not limited to keyboards, displays, pointing devices, etc.) may be coupled to the system either directly or through intervening I/O controllers 2710.

Network adapters 2708 may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters 2708.

The computer architecture 2700 may be coupled to storage 2716 (e.g. a non-volatile storage area, such as magnetic disk drives, optical disk drives, a tape drive, etc.). The storage 2716 may comprise an internal storage device or an attached or network accessible storage. Computer programs 2706 in storage 2716 may be loaded into the memory elements 2704 and executed by a processor 2702 in a manner known in the art.

The computer architecture 2700 may include fewer components than illustrated, additional components not illustrated herein, or some combination of the components illustrated and additional components. The computer architecture 2700 may comprise any computing device known in the art, such as a mainframe, server, personal computer, workstation, laptop, handheld computer, telephony device, network appliance, virtualization device, storage controller, etc.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of embodiments of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.

The foregoing description of embodiments of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the embodiments to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the embodiments be limited not by this detailed description, but rather by the claims appended hereto. The above specification, examples and data provide a complete description of the manufacture and use of the composition of the embodiments. Since many embodiments may be made without departing from the spirit and scope of the invention, the embodiments reside in the claims hereinafter appended or any subsequently-filed claims, and their equivalents. 

1. A method for processing Business Intelligence (BI) reports, comprising: providing, using a computing device including a processor, a set of BI meta model templates and BI report specification templates; selecting a Business Intelligence (BI) meta model template from the set of BI meta model templates based on one or more meta model object types in a model structure; generating a BI meta model from the selected BI meta model template; selecting one or more BI report specification templates from the set of BI report specification templates based on a mining model type; generating a BI report specification from the selected one or more BI report specification templates, a schema of the model structure, and content of the model structure; deploying the BI meta model and the BI report specification to a BI server for use in generating a BI report using a BI tool at the BI server; and in response to a user request for a BI report, generating the BI report with a BI tool at the BI server that uses the BI meta model and the BI report specification.
 2. The method of claim 1, further comprising: preparing data for data mining; creating the mining model by modeling the prepared data; and extracting content of the mining model into the model structure.
 3. The method of claim 1, wherein generating the BI meta model further comprises: analyzing a structure of the model structure; and generating information defining one or more meta model objects.
 4. The method of claim 1, wherein generating the BI report specification further comprises: analyzing the schema of the model structure and the content of the model structure; and generating information defining a structure and content of the BI report based on the analyzing and based on the selected BI report specification template.
 5. The method of claim 1, further comprising: generating a piece of executable code that is invoked to dynamically and repeatedly create the mining model by modeling data in one or more data structures and to extract the content of the mining model into the model structure.
 6. The method of claim 5, further comprising: storing the executable code in a data store coupled to the computing device; and at the BI server, invoking the executable code with one or more data mining parameters provided by a user to create the mining model by modeling data in one or more data structures and to extract the content of the mining model into the model structure.
 7. The method of claim 1, wherein the mining model type is one of clustering, classification, association, regression, sequence rules, and time series.
 8. The method of claim 1, wherein the meta model object type is an element of a model. 9-24. (canceled) 